# Import
import pandas as pd
import nltk
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import torch
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from transformers import BertForSequenceClassification, AdamW, BertConfig
from transformers import BertTokenizer
from keras_preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report as clsr
from sklearn.metrics import confusion_matrix, cohen_kappa_score, plot_confusion_matrix
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.manifold import TSNE
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU')
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
There are 1 GPU(s) available. We will use the GPU
df = pd.read_csv("../data/all_clean.csv")
text=df['text']
sp500_mean_label=df['sp500_mean_label']
sp500_last_label=df['sp500_last_label']
usdx_mean_label=df['usdx_mean_label']
usdx_last_label=df['usdx_last_label']
label_to_index = {}
index_to_label = {}
label_list = list(set(sp500_mean_label))
for i in range(len(label_list)):
label = label_list[i]
label_to_index[label] = i
index_to_label[i] = label
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
input_ids = []
for text in text:
encoded_sent = tokenizer.encode(text,
add_special_tokens=True,
max_length=128,
truncation=True)
input_ids.append(encoded_sent)
input_ids = pad_sequences(input_ids, maxlen=128, dtype="long",
value=0, truncating="post", padding="post")
attention_masks = []
for sent in input_ids:
att_mask = [int(token_id > 0) for token_id in sent]
attention_masks.append(att_mask)
labels = [label_to_index[label] for label in sp500_mean_label]
train_input_ids, test_input_ids, train_labels, test_labels = train_test_split(input_ids, labels, test_size=0.2, random_state=42)
train_attention_masks, test_attention_masks, train_labels, test_labels = train_test_split(attention_masks, labels, test_size=0.2, random_state=42)
train_input_ids = torch.tensor(train_input_ids)
test_input_ids = torch.tensor(test_input_ids)
train_labels = torch.tensor(train_labels)
test_labels = torch.tensor(test_labels)
train_attention_masks = torch.tensor(train_attention_masks)
test_attention_masks = torch.tensor(test_attention_masks)
batch_size = 32
# Create the DataLoader for our training set.
train_data = TensorDataset(train_input_ids, train_attention_masks, train_labels)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)
# Create the DataLoader for our test set.
test_data = TensorDataset(test_input_ids, test_attention_masks, test_labels)
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
model = BertForSequenceClassification.from_pretrained(
"bert-base-uncased",
num_labels = len(label_list),
output_attentions = False,
output_hidden_states = False
)
model.cuda()
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight'] - This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
BertForSequenceClassification(
(bert): BertModel(
(embeddings): BertEmbeddings(
(word_embeddings): Embedding(30522, 768, padding_idx=0)
(position_embeddings): Embedding(512, 768)
(token_type_embeddings): Embedding(2, 768)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): BertEncoder(
(layer): ModuleList(
(0): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(1): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(2): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(3): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(4): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(5): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(6): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(7): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(8): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(9): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(10): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(11): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): BertPooler(
(dense): Linear(in_features=768, out_features=768, bias=True)
(activation): Tanh()
)
)
(dropout): Dropout(p=0.1, inplace=False)
(classifier): Linear(in_features=768, out_features=2, bias=True)
)
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU')
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
There are 1 GPU(s) available. We will use the GPU
optimizer = AdamW(model.parameters(),
lr = 1e-5,
eps = 1e-8)
c:\Program Files\Python39\lib\site-packages\transformers\optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning warnings.warn(
dim_reducer = TSNE(n_components=2, init='pca', learning_rate=200.0)
def visualize_layerwise_embeddings(hidden_states,masks,labels,epoch,title,layers_to_visualize):
!mkdir -p /tmp/plots/{title}
num_layers = len(layers_to_visualize)
fig = plt.figure(figsize=(30,20)) #each subplot of size 6x6, each row will hold 4 plots
title_name = "Epoch "+str(epoch)+" Embeddings"
plt.suptitle(title_name, fontsize = 30, y = 0.92)
labels = labels.numpy().reshape(-1)
#Pic = pd.DataFrame()
for i,layer_i in enumerate(layers_to_visualize):
layer_embeds = hidden_states[layer_i]
layer_averaged_hidden_states = torch.div(layer_embeds.sum(dim=1),masks.sum(dim=1,keepdim=True))
layer_dim_reduced_embeds = dim_reducer.fit_transform(layer_averaged_hidden_states.detach().numpy())
df = pd.DataFrame.from_dict({'x':layer_dim_reduced_embeds[:,0],'y':layer_dim_reduced_embeds[:,1],'label':labels})
#Pic = pd.concat([Pic,df],ignore_index = True)
fig = plt.subplot(331+i)
plt.subplots_adjust(wspace =0.4, hspace =0.4)
sns.scatterplot(data=df,x='x',y='y',hue='label')
name = "layer "+str(layer_i+1)
plt.title(name, fontsize = 20)
#sns.scatterplot(data=Pic,x='x',y='y',hue='label')
plt.savefig('vis'+str(epoch)+'.png')
from tqdm import tqdm
average_loss = []
for epoch_i in range(20):
print("")
print('Epoch {}'.format(epoch_i + 1))
print('Training...')
total_loss = 0
model.train()
train_masks,train_ys = torch.zeros(0,128),torch.zeros(0,1)
train_hidden_states = None
progress = tqdm(range(len(train_dataloader)), position = 0)
for batch in train_dataloader:
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_mask, b_labels = batch
model.zero_grad()
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels,
output_hidden_states=True,
return_dict=True)
loss = outputs[0]
total_loss += loss.item()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
hidden_states = outputs.hidden_states[1:]
train_masks = torch.cat([train_masks,b_input_mask.cpu()])
train_ys = torch.cat([train_ys,b_labels.cpu().view(-1,1)])
if type(train_hidden_states) == type(None):
train_hidden_states = tuple(layer_hidden_states.cpu() for layer_hidden_states in hidden_states)
else:
train_hidden_states = tuple(torch.cat([layer_hidden_state_all,layer_hidden_state_batch.cpu()])for layer_hidden_state_all,layer_hidden_state_batch in zip(train_hidden_states,hidden_states))
progress.update(1)
avg_train_loss = total_loss / len(train_dataloader)
average_loss.append(avg_train_loss)
print("Average training loss: {0:.2f}".format(avg_train_loss))
visualize_layerwise_embeddings(hidden_states=train_hidden_states,
masks=train_masks,
labels=train_ys,
epoch=epoch_i,
title='train_data',
layers_to_visualize=[0,1,2,3,4,8,9,10,11])
Epoch 1 Training...
100%|██████████| 168/168 [05:17<00:00, 3.56s/it]
Average training loss: 0.69
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 2 Training...
100%|██████████| 168/168 [07:40<00:00, 2.74s/it] 100%|██████████| 168/168 [05:09<00:00, 3.66s/it]
Average training loss: 0.68
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 3 Training...
100%|██████████| 168/168 [07:30<00:00, 2.68s/it] 100%|██████████| 168/168 [05:07<00:00, 3.41s/it]
Average training loss: 0.64
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 4 Training...
100%|██████████| 168/168 [07:15<00:00, 2.59s/it] 100%|██████████| 168/168 [05:03<00:00, 3.42s/it]
Average training loss: 0.57
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 5 Training...
100%|██████████| 168/168 [07:13<00:00, 2.58s/it] 100%|██████████| 168/168 [05:03<00:00, 3.18s/it]
Average training loss: 0.49
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 6 Training...
100%|██████████| 168/168 [07:14<00:00, 2.59s/it] 100%|██████████| 168/168 [05:07<00:00, 3.44s/it]
Average training loss: 0.41
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 7 Training...
100%|██████████| 168/168 [07:21<00:00, 2.63s/it] 100%|██████████| 168/168 [05:15<00:00, 3.30s/it]
Average training loss: 0.35
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 8 Training...
100%|██████████| 168/168 [07:32<00:00, 2.69s/it] 100%|██████████| 168/168 [05:10<00:00, 3.43s/it]
Average training loss: 0.27
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 9 Training...
100%|██████████| 168/168 [07:37<00:00, 2.72s/it] 100%|██████████| 168/168 [05:04<00:00, 3.35s/it]
Average training loss: 0.22
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 10 Training...
100%|██████████| 168/168 [07:33<00:00, 2.70s/it] 100%|██████████| 168/168 [05:02<00:00, 3.27s/it]
Average training loss: 0.19
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 11 Training...
100%|██████████| 168/168 [07:26<00:00, 2.66s/it] 100%|██████████| 168/168 [05:09<00:00, 3.42s/it]
Average training loss: 0.14
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 12 Training...
100%|██████████| 168/168 [07:25<00:00, 2.65s/it] 100%|██████████| 168/168 [04:57<00:00, 3.24s/it]
Average training loss: 0.13
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 13 Training...
100%|██████████| 168/168 [07:00<00:00, 2.51s/it] 100%|██████████| 168/168 [06:22<00:00, 5.07s/it]
Average training loss: 0.10
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 14 Training...
100%|██████████| 168/168 [09:53<00:00, 3.53s/it] 100%|██████████| 168/168 [06:46<00:00, 5.10s/it]
Average training loss: 0.10
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 15 Training...
100%|██████████| 168/168 [10:24<00:00, 3.72s/it] 100%|██████████| 168/168 [06:51<00:00, 5.20s/it]
Average training loss: 0.09
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 16 Training...
100%|██████████| 168/168 [10:23<00:00, 3.71s/it] 100%|██████████| 168/168 [06:48<00:00, 5.05s/it]
Average training loss: 0.07
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 17 Training...
100%|██████████| 168/168 [10:29<00:00, 3.75s/it] 100%|██████████| 168/168 [07:03<00:00, 5.32s/it]
Average training loss: 0.06
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 18 Training...
100%|██████████| 168/168 [10:23<00:00, 3.71s/it] 100%|██████████| 168/168 [06:56<00:00, 5.04s/it]
Average training loss: 0.09
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 19 Training...
100%|██████████| 168/168 [10:34<00:00, 3.78s/it] 100%|██████████| 168/168 [07:04<00:00, 5.27s/it]
Average training loss: 0.06
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(
Epoch 20 Training...
100%|██████████| 168/168 [10:39<00:00, 3.81s/it] 100%|██████████| 168/168 [07:00<00:00, 5.10s/it]
Average training loss: 0.05
The syntax of the command is incorrect. c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn( c:\Program Files\Python39\lib\site-packages\sklearn\manifold\_t_sne.py:996: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence. warnings.warn(